This dashboard has been created according to worldometers.info design. In order to compensate with the lack of real time statistical data in Egypt, the given data below will be updated daily at 10:00 PM (Cairo Time). Stay Home, Stay Safe.
Weekly Distribution of New Confirmed Cases
Weekly Distribution of New Recovered Cases
Weekly Distribution of New Deaths
---
title: "Covid-19 in Egypt"
date: "Last update: `r Sys.time()`"
author: Sherif Embarak^[https://github.com/Sherif-Embarak/]
output:
flexdashboard::flex_dashboard:
social: menu
source: embed
vertical_layout: scroll
orientation: rows
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
rm(list=ls(all=TRUE))
options(stringsAsFactors = FALSE)
library(ggplot2)
library(plotly)
library(flexdashboard)
library(DT)
library(plotly)
library(knitr)
library(lubridate)
library(crosstalk)
setwd("D:/work/git corona/")
df <- read.csv("eg_covid.csv")
df$Day <- mdy(df$Day)
df$AccDay <- df$Day
df$Day <- paste(day(df$Day) , months.Date(df$Day) )
df$Curfew <- as.character(df$Curfew)
df$index <- as.numeric(rownames(df))
########################################################################
#weekly summary
AllDF <- df
shift <- 4
summary_AllDF <- data.frame(AllDF %>% mutate(week = ((as.numeric(AccDay)+shift) %/% 7) - ((as.numeric(min(AccDay))+shift) %/% 7)) %>%
group_by(week) %>%
summarise(number_of_days= table(week), from = min(AccDay) , to = max(AccDay), sum(New.Cases) , sum(New.Deaths), sum(New.Recovered)))
summary_AllDF$number_of_days <- as.integer(summary_AllDF$number_of_days)
########################################################################
df <- df[,c(1:8,12)]
attach(df)
next_day <- max(index)+1
nd_new_case <-as.integer(exp(predict(glm(New.Cases~index ,family = gaussian(link = 'log') ),list(index=c(next_day)))))
nd_new_deaths <-as.integer(exp(predict(glm(New.Deaths+0.01~index ,family = gaussian(link = 'log') ),list(index=c(next_day)))))
nd_new_recoverd <-as.integer(exp(predict(glm(New.Recovered+0.01~index ,family = gaussian(link = 'log') ),list(index=c(next_day)))))
total_cases <- df$Total.Cases[nrow(df)]
total_deaths <- df$Total.Deaths[nrow(df)]
total_recovered <- df$Total.Recovered[nrow(df)]
closed <- total_deaths+total_recovered
active <- total_cases - closed
df$Day <- factor(df$Day, levels = df$Day)
xlabel <- df$Day[as.integer(seq(1 , nrow(df) , length.out = 10))]
hk <- highlight_key(df, ~Total.Cases)
chart1 <-ggplotly(ggplot(hk, aes(x=Day, y=New.Cases)) + scale_x_discrete(breaks = xlabel)+
geom_bar(width = 0.3, stat = "identity")+scale_fill_manual(values = c("#6698FF", "#153E7E"))+
theme(panel.grid.major.x = element_blank(), axis.text.x = element_text(angle = 70, hjust = 1))+
geom_smooth(aes(x=index, y=New.Cases), method = "glm", formula = y~x,method.args = list(family = gaussian(link = 'log')))+
geom_text(label=paste("Tomorow's predication:",nd_new_case ),
x=10,
y=max(New.Cases)
)
,tooltip = c("x", "y")) %>%
highlight(off = "plotly_relayout")
chart2 <-ggplotly(ggplot(hk, aes(x=Day, y=New.Deaths)) + scale_x_discrete(breaks = xlabel)+
geom_bar(width = 0.3, stat = "identity")+scale_fill_manual(values = c("#6698FF", "#153E7E"))+
theme(panel.grid.major.x = element_blank(), axis.text.x = element_text(angle = 70, hjust = 1))+
geom_smooth(aes(x=index, y=New.Deaths+0.01), method = "glm", formula = y~x,method.args = list(family = gaussian(link = 'log')))+
geom_text(label=paste("Tomorow's predication:",nd_new_deaths ),
x=10,
y=max(New.Deaths)
)
,tooltip = c("x", "y")) %>%
highlight(off = "plotly_relayout")
chart3 <-ggplotly(ggplot(hk, aes(x=Day, y=New.Recovered)) + scale_x_discrete(breaks = xlabel)+
geom_bar(width = 0.3, stat = "identity")+scale_fill_manual(values = c("#6698FF", "#153E7E"))+
theme(panel.grid.major.x = element_blank(), axis.text.x = element_text(angle = 70, hjust = 1))+
geom_smooth(data = hk, aes(x=index, y=New.Recovered+0.01), method = "glm", formula = y~x,method.args = list(family = gaussian(link = 'log')))+
geom_text(label=paste("Tomorow's predication:",nd_new_recoverd ),
x=10,
y=max(New.Recovered)
)
,tooltip = c("x", "y")) %>%
highlight(off = "plotly_relayout")
chart4 <- ggplotly(ggplot(data=hk, aes(x=Day, y=Total.Cases, group=1))+ scale_x_discrete(breaks = xlabel)+
geom_line(color="#33CCFF", size=1)+theme(panel.grid.major.x = element_blank(), axis.text.x = element_text(angle = 70, hjust = 1))+
geom_point(color="#33CCFF"),tooltip = c("x", "y")) %>%
highlight(off = "plotly_relayout")
chart5 <- ggplotly(ggplot(data=hk, aes(x=Day, y=Total.Deaths, group=1))+ scale_x_discrete(breaks = xlabel)+
geom_line(color="#FF9900", size=1)+theme(panel.grid.major.x = element_blank(), axis.text.x = element_text(angle = 70, hjust = 1))+
geom_point(color="#FF9900"),tooltip = c("x", "y")) %>%
highlight(off = "plotly_relayout")
chart6 <- ggplotly(ggplot(data=hk, aes(x=Day, y=Total.Recovered, group=1))+ scale_x_discrete(breaks = xlabel)+
geom_line(color="#00DDDD", size=1)+theme(panel.grid.major.x = element_blank(), axis.text.x = element_text(angle = 70, hjust = 1))+
geom_point(color="#00DDDD"),tooltip = c("x", "y"))%>%
highlight(off = "plotly_relayout")
```
Daily Dashboard
=======================================================================
This dashboard has been created according to worldometers.info design. In order to compensate with the lack of real time statistical data in Egypt, the given data below will be updated daily at 10:00 PM (Cairo Time).
Stay Home, Stay Safe.
Row
-----------------------------------------------------------------------
### Total Coronavirus Cases in Egypt
```{r, echo=FALSE}
valueBox(total_cases)
```
### Total Coronavirus Deaths in Egypt
```{r, echo=FALSE}
valueBox(total_deaths, color="warning")
```
### Total Coronavirus Recovered Cases in Egypt
```{r, echo=FALSE}
valueBox(total_recovered , color = "#00DDDD")
```
### Cases which had an outcome `r ""` `r paste0("Deaths/Discharged : ", total_deaths, " (", round((total_deaths/closed)*100,1),"%)" )` `r ""` `r paste0("Recovered/Discharged : ", total_recovered, " (", round((total_recovered/closed)*100,1),"%)" )`
```{r, echo=FALSE}
valueBox(paste("Closed Cases: ",closed))
```
### Currently Infected Patients
```{r, echo=FALSE}
valueBox(paste("Active Cases: ",active))
```
Row
-------------------------------------
### Distibution of Cases
```{r, echo=FALSE }
DT::datatable(hk,class = 'cell-border stripe hover compact', rownames = FALSE , options = list(pageLength = 1,order = list(2, 'desc')))%>%
formatStyle('New.Cases', backgroundColor = '#FFEEAA') %>%
formatStyle('New.Deaths',backgroundColor = 'red')%>%
formatStyle(names(df),fontWeight = 'bold')%>%
formatStyle('Curfew', backgroundColor = styleEqual(c(0, 1), c('#a6cee3', '#1f78b4')))%>%
highlight(on = "plotly_click" , off ="plotly_doubleclick")
```
Row
-------------------------------------
### Confirmed Cases per day
```{r, echo=FALSE ,fig.height=3}
chart1
```
### Deaths per day
```{r, echo=FALSE ,fig.height=3}
chart2
```
### Recovered Cases per day
```{r, echo=FALSE ,fig.height=3}
chart3
```
Row
-------------------------------------
### Total Confirmed Cases
```{r, echo=FALSE, warning=FALSE,message=FALSE,results='asis',fig.show='asis'}
chart4
```
### Total Deaths
```{r, echo=FALSE, warning=FALSE,message=FALSE,results='asis',fig.show='asis'}
chart5
```
### Total Recovered
```{r, echo=FALSE, warning=FALSE,message=FALSE,results='asis',fig.show='asis'}
chart6
```
Weekly Dashboard
=======================================================================
```{r, include=FALSE}
weekly_hk <- highlight_key(summary_AllDF, ~week)
chart7 <- ggplotly(ggplot(weekly_hk, aes(x=week , y=sum.New.Cases. , text =paste("from:", from, "to:",to))) +
geom_bar(width = 0.4 , stat = "identity" , fill="steelblue")+
geom_text(aes(label=sum.New.Cases.), color="black", size=3.5)+
theme(panel.grid.major.x = element_blank()),tooltip = c("x", "y" , "text")) %>%
highlight(off = "plotly_relayout")
chart8 <-ggplotly(ggplot(weekly_hk, aes(x=week , y=sum.New.Deaths. , text =paste("from:", from, "to:",to))) +
geom_bar(width = 0.4 , stat = "identity", fill="steelblue")+
geom_text(aes(label=sum.New.Deaths.), color="black", size=3.5)+
theme(panel.grid.major.x = element_blank()),tooltip = c("x", "y" , "text")) %>%
highlight(off = "plotly_relayout")
chart9 <-ggplotly(ggplot(weekly_hk, aes(x=week , y=sum.New.Recovered. , text =paste("from:", from, "to:",to))) +
geom_bar(width = 0.4 , stat = "identity", fill="steelblue")+
geom_text(aes(label=sum.New.Recovered.), color="black", size=3.5)+
theme(panel.grid.major.x = element_blank()),tooltip = c("x", "y" , "text")) %>%
highlight(off = "plotly_relayout")
```
Row
-------------------------------------
### Chart 1
```{r, echo=FALSE , fig.height = 2, fig.width = 2}
DT::datatable(weekly_hk)%>%
highlight(on = "plotly_click" , off ="plotly_doubleclick")
```
Row
-------------------------------------
### Chart 2
```{r, echo=FALSE}
cat("Weekly Distribution of New Confirmed Cases")
chart7
```
### Chart 3
```{r, echo=FALSE}
cat("Weekly Distribution of New Recovered Cases")
chart8
```
### Chart 4
```{r, echo=FALSE}
cat("Weekly Distribution of New Deaths")
chart9
```